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1) Trainer

	Reads given train data and trains it with BOWKMeansTrainer and 
	extracts  descriptors  with BOWImgDescriptorExtractor. As soon 
	as training is done, application will save the state to a file.

2) Classifier
	
	While  classifying first  image,  the application will load the 
	saved BOWKMeansTrainer and BOWImgDescriptorExtractor. This step
	needs to be executed only once.  Application  immediately tries
	to classify given image and write the prediction. 

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OpenCV's features extractor, descriptors and classifiers used:

FlannBased DescriptorMatcher and SURF DescriptorExtractor were used in BOWImgDescriptorExtractor, in order to perform "BagOfWords" based image classification.

SURF FeatureDetector was also used to detect features.

Train images were described with BOWImgDescriptorExtractor and trained with BOWKMeansTrainer.

At the end NormalBayesClassifier was trained, in order to be able to predict classes of given images.

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